Currently I am teaching the following courses:

__Undergraduate courses__

"* Combinatorial Optimization"* (elective), of the 8th semester.

This course aims to an introduction to network optimization models and integer programming. Emphasis will be given on problem modeling and network optimization applications for the design of large-scale networks. The course covers the following topics: the shortest path problem, the minimum spanning tree problem, the maximum flow problem, and the minimum cost network flow problem. Furthermore, the student will be introduced to modeling and solution techniques for integer programming problems, branch & bound algorithm, dynamic programming, and special problems such as the Steiner tree problem and the traveling salesman problem (TSP). The student, apart from the methodology in each section, will learn how to use state-of-the-art optimization software packages such as the

*CPLEX*&

*Gurobi*solvers and the modeling language

*AMPL*.

http://opencourses.gr/opencourse.xhtml?id=15611 |

"** Linear Algebra**" (mandatory), of the 1st semester. This course aims to an introduction to the basic concepts and methods of Linear Algebra with SageMath. The course covers the following topics: matrices, linear systems, vector spaces, applications, linear transformations, and eigenvalues - eigenvectors. The student, apart from the methodology in each section, will learn how to use SageMath for linear algebra operations.

"** Operational Research**" (mandatory), of the 4th semester.
The role of operational research (O.R.) in decision making. Introduction and basic concepts of model development and linear programming. Graphical solution procedure of a linear model & special cases, linear programming applications – case studies, optimization software packages (e.g., Lingo, POM-QM for Windows, Excel Solver), the Simplex algorithm, duality theory and economic interpretation, sensitivity analysis. Introduction to network optimization models. The transportation problem and its applications in practice.

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**Note**: Since 2016, the course syllabus has been changedhttp://opencourses.gr/opencourse.xhtml?id=15655 |

"** Decision Making Models**" (elective), of the 7th semester.
This course aims to an introduction to problem solving and decision making in complex business problems, through the methodology of management science. Emphasis will be given to the understanding of the decision models under risk and uncertainty and their applications in technology management – case studies. The course covers the following topics: decision theory & criteria, expected value of perfect information, utility theory, decision trees & sensitivity analysis, introduction to game theory, performance measurement using data envelopment analysis (DEA). The student, apart from the methodology in each section, will learn how to use state-of-the-art software packages (e.g., Palisade DecisionTools Suite, DEA Solver). (

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**Note**: Since 2015, this course is no longer being taughthttp://opencourses.gr/opencourse.xhtml?id=15613 |

"** Supply Chain Management**" (mandatory), of the 6th semester. This course aims to an introduction to supply chain management. Emphasis will be given to the presentation of mathematical models and solution methods that assist with supply chain design, planning & optimization. The course covers the following topics: supply chain design, inventory management systems, materials requirement planning, facility location models, vehicle routing and scheduling, distribution planning, and decision support systems for supply chain management. The student, apart from the methodology in each section, will learn how to use modern educational software packages.

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**Note**: Since 2015, this course is no longer being taught by mehttp://opencourses.gr/opencourse.xhtml?id=15673 |

__Graduate courses (MSc Level) __

"* Heuristic Methods"* (elective), of the 1st semester.

This course aims to an introduction to modern heuristic methods, with an emphasis on computationally hard combinatorial and global optimization problems. Basic concepts such as exhaustive search methods, solution representation, local search, neighborhoods, and local optimal, will be analyticall presented. The course covers the following topics: variable neighborhood search, genetic algorithms, nature inspired algorithms, (e.g., swarm intelligence), tabu search, and simulated annealing. Furthermore, applications of metaheuristic algorithms (e.g., in routing and inventory problems) will be shown. Finally, statistical analysis of computational experiments of heuristics will also be discussed.

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**Note**: Since 2022, this course is no longer being taughthttp://opencourses.gr/opencourse.xhtml?id=15667 |

*The teaching materials for each course are available through Open eClass
Please also note that,
the above
lectures included live Q&A sessions with audience participation.*